# V-Doc : Visual questions answers with Documents

Source: [V-Doc : Visual questions answers with Documents](<https://www.sota2.com/research/paper/68fa6ca11e209131cea35139>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-01T14:39:28.702Z

Authors: Yihao Ding, Zhe Huang, Runlin Wang, Yanhang Zhang, Xianru Chen, Yuzhong Ma, Hyunsuk Chung, Soyeon Caren Han

Published: 2022-05-27T02:38:09.000Z

## Abstract

We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks. The V-Doc supports generating and using both extractive and abstractive question-answer pairs using documents images. The extractive QA selects a subset of tokens or phrases from the document contents to predict the answers, while the abstractive QA recognises the language in the content and generates the answer based on the trained model. Both aspects are crucial to understanding the documents, especially in an image format. We include a detailed scenario of question generation for the abstractive QA task. V-Doc supports a wide range of datasets and models, and is highly extensible through a declarative, framework-agnostic platform.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.13724>)

[PDF](<https://arxiv.org/pdf/2205.13724.pdf>)

[Implementation](<https://github.com/usydnlp/vdoc>)